Cancer metabolomics in basic science perspective

A critical appraisal of the role of metabolomics in breast cancer research and diagnostics

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning

Background

Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis.

Methods

Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models.

Results

The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum-paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved.

Conclusions

In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

Glycerol 3-phosphate dehydrogenases (1 and 2) in cancer and other diseases

The glycerol 3-phosphate shuttle (GPS) is composed of two different enzymes: cytosolic NAD+-linked glycerol 3-phosphate dehydrogenase 1 (GPD1) and mitochondrial FAD-linked glycerol 3-phosphate dehydrogenase 2 (GPD2). These two enzymes work together to act as an NADH shuttle for mitochondrial bioenergetics and function as an important bridge between glucose and lipid metabolism. Since these genes were discovered in the 1960s, their abnormal expression has been described in various metabolic diseases and tumors. Nevertheless, it took a long time until scientists could investigate the causal relationship of these enzymes in those pathophysiological conditions. To date, numerous studies have explored the involvement and mechanisms of GPD1 and GPD2 in cancer and other diseases, encompassing reports of controversial and non-conventional mechanisms. In this review, we summarize and update current knowledge regarding the functions and effects of GPS to provide an overview of how the enzymes influence disease conditions. The potential and challenges of developing therapeutic strategies targeting these enzymes are also discussed.

Metabolic Fingerprint in Childhood Acute Lymphoblastic Leukemia

INTRODUCTION

Acute lymphoblastic leukemia (ALL) is the most prevalent childhood malignancy. Despite high cure rates, several questions remain regarding predisposition, response to treatment, and prognosis of the disease. The role of intermediary metabolism in the individualized mechanistic pathways of the disease is unclear. We have hypothesized that children with any (sub)type of ALL have a distinct metabolomic fingerprint at diagnosis when compared: (i) to a control group; (ii) to children with a different (sub)type of ALL; (iii) to the end of the induction treatment.

MATERIALS AND METHODS

In this prospective case-control study (NCT03035344), plasma and urinary metabolites were analyzed in 34 children with ALL before the beginning (D0) and at the end of the induction treatment (D33). Their metabolic fingerprint was defined by targeted analysis of 106 metabolites and compared to that of an equal number of matched controls. Multivariate and univariate statistical analyses were performed using SIMCAP and scripts under the R programming language.

RESULTS

Metabolomic analysis showed distinct changes in patients with ALL compared to controls on both D0 and D33. The metabolomic fingerprint within the patient group differed significantly between common B-ALL and pre-B ALL and between D0 and D33, reflecting the effect of treatment. We have further identified the major components of this metabolic dysregulation, indicating shifts in fatty acid synthesis, transfer and oxidation, in amino acid and glycerophospholipid metabolism, and in the glutaminolysis/TCA cycle.

CONCLUSIONS

The disease type and time point-specific metabolic alterations observed in pediatric ALL are of particular interest as they may offer potential for the discovery of new prognostic biomarkers and therapeutic targets.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Discovery of metabolomic biomarkers for discriminating platinum-sensitive and platinum-resistant ovarian cancer by using GC-MS

This study aims to determine ovarian cancer (OC) patients with platinum resistance for alternative treatment protocols by using metabolomic methodologies. Urine and serum samples of platinum-resistant and platinum-sensitive OC were analyzed using GC-MS. After data processing of GC-MS raw data, multivariate analyses were performed to interpret complex data for biologically meaningful information and to identify the biomarkers that cause differences between two groups. The biomarkers were verified after univariate, multivariate, and ROC analysis. Finally, metabolomic pathways related to group separations were specified. The results of biomarker analysis showed that 3,4-dihydroxyphenylacetic acid, 4-hydroxybutyric acid, L-threonine, D- mannose, and sorbitol metabolites were potential biomarkers in urine samples. In serum samples, L-arginine, linoleic acid, L-glutamine, and hypoxanthine were identified as important biomarkers. R2Y, Q2, AUC, sensitivity and specificity values of platinum-resistant and sensitive OC patients' urine and serum samples were 0.85, 0.545, 0.844, 91.30%, 81.08 and 0.570, 0.206, 0.743, 77.78%, 74.28%, respectively. In metabolic pathway analysis of urine samples, tyrosine metabolism and fructose and mannose metabolism were found to be statistically significant (p < 0.05) for the discrimination of the two groups. While 3,4-dihydroxyphenylacetic acid, L-tyrosine, and fumaric acid metabolites were effective in tyrosine metabolism. D-sorbitol and D-mannose metabolites were significantly important in fructose and mannose metabolism. However, seven metabolomic pathways were significant (p < 0.05) in serum samples. In terms of p-value, L-glutamine in the nitrogen metabolic pathway from the first three pathways; L-glutamine and pyroglutamic acid metabolites in D-glutamine and D-glutamate metabolism. In the arginine and proline metabolic pathway, L-arginine, L-proline, and L-ornithine metabolites differed significantly between the two groups.

Noninvasive urine metabolomics of prostate cancer and its therapeutic approaches: a current scenario and future perspective

INTRODUCTION

The sensitive, specific, fast, robust and noninvasive biomarkers for the evaluation of prostate cancer (PC) remain elusive in medical research. However, efforts are in full sway to investigate and resolve these puzzles for clinical practice. Advances in modern analytical techniques, sample processing, and the emergence of multiple omics approaches have created a great hope for the development of better detection modalities for PC. The objective of the present review is to provide a concise overview of the PC metabolomics-based potential discriminating molecules in urine samples using nuclear magnetic resonance spectroscopy and mass spectrometry.

AREA COVERED

A literature search was executed to find the studies reporting the noninvasive urine-based biomarkers for the diagnosis and prognosis of underlying disease. Most studies have extensivelyreported PC discriminating molecules with their respective controls. Additionally, pathophysiology and the treatment paradigm of PC are summarized and related to the insights underpinning the therapeutic intervention of PC.

EXPERT OPINION

With multi-centric, global, comprehensive omics approaches via either a non- or least-invasive bio-matrix may open new avenues of research for PC biomarker discovery, backed by a molecular mechanistic outline.

Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development

Prostate cancer (PCa) is a global health problem that affects millions of men every year. In the past decade, metabolomics and related subareas, such as lipidomics, have demonstrated an enormous potential to identify novel mechanisms underlying PCa development and progression, providing a good basis for the development of new and more effective therapies and diagnostics. In this study, a multiplatform metabolomics and lipidomics approach, combining untargeted mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based techniques, was applied to PCa tissues to investigate dysregulations associated with PCa development, in a cohort of 40 patients submitted to radical prostatectomy for PCa. Results revealed significant alterations in the levels of 26 metabolites and 21 phospholipid species in PCa tissue compared with adjacent nonmalignant tissue, suggesting dysregulation in 13 metabolic pathways associated with PCa development. The most affected metabolic pathways were amino acid metabolism, nicotinate and nicotinamide metabolism, purine metabolism, and glycerophospholipid metabolism. A clear interconnection between metabolites and phospholipid species participating in these pathways was observed through correlation analysis. Overall, these dysregulations may reflect the reprogramming of metabolic responses to produce high levels of cellular building blocks required for rapid PCa cell proliferation.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.

Integration of metabolomic and transcriptomic profiles to identify biomarkers in serum of lung cancer

We used blood serum samples collected from 31 lung cancer (LC) patients and 29 healthy volunteers in this study. Levels of serum metabolites were qualitative quantified with gas chromatography-mass spectrometry (GC-MS), and the data were analyzed by partial least-squares discrimination analysis (PLS-DA). Based on the Kyoto Encyclopedia of Genes and Genomes database, we performed pathway-based analysis utilizing metabolites presented at differential abundance between the LC serum samples and the normal healthy serum samples for systematical investigation on the metabolic alterations associated with LC pathogenesis. Finally, we analyzed the significantly enriched pathways as well as their relevant differentially expressed messenger RNAs, and drawn a correlation network plot to identify the serum metabolic biomarkers and the significantly altered metabolic pathways for LC. GC-MS analysis showed that 23 of the 169 metabolites identified were significantly different. PLS-DA model revealed that 13 of these metabolites were with variable importance > 1, and particularly five were with area under curve > 0.9. Pathway-based analysis demonstrated that five of eight enriched metabolic pathways were statistically significant with false discovery rate < 0.05. Lastly, the correlation networks between these pathways and their related genes suggested that 29 genes had correlation degree > 10, which were mainly engaged in the purine metabolism. In conclusion, we identified indole-3-lactate, erythritol, adenosine-5-phosphate, paracetamol and threitol as serum metabolic biomarkers for LC through metabolomics analysis. Besides, we identified the purine metabolism as the significantly altered metabolic pathway in LC with the help of transcriptomics analysis.

Delta-Tocotrienol Modulates Glutamine Dependence by Inhibiting ASCT2 and LAT1 Transporters in Non-Small Cell Lung Cancer (NSCLC) Cells: A Metabolomic Approach

The growth and development of non-small cell lung cancer (NSCLC) primarily depends on glutamine. Both glutamine and essential amino acids (EAAs) have been reported to upregulate mTOR in NSCLC, which is a bioenergetics sensor involved in the regulation of cell growth, cell survival, and protein synthesis. Seen as novel concepts in cancer development, ASCT2 and LAT transporters allow glutamine and EAAs to enter proliferating tumors as well as send a regulatory signal to mTOR. Blocking or downregulating these glutamine transporters in order to inhibit glutamine uptake would be an excellent therapeutic target for treatment of NSCLC. This study aimed to validate the metabolic dysregulation of glutamine and its derivatives in NSCLC using cellular 1H-NMR metabolomic approach while exploring the mechanism of delta-tocotrienol (δT) on glutamine transporters, and mTOR pathway. Cellular metabolomics analysis showed significant inhibition in the uptake of glutamine, its derivatives glutamate and glutathione, and some EAAs in both cell lines with δT treatment. Inhibition of glutamine transporters (ASCT2 and LAT1) and mTOR pathway proteins (P-mTOR and p-4EBP1) was evident in Western blot analysis in a dose-dependent manner. Our findings suggest that δT inhibits glutamine transporters, thus inhibiting glutamine uptake into proliferating cells, which results in the inhibition of cell proliferation and induction of apoptosis via downregulation of the mTOR pathway.

Oncometabolites as biomarkers in thyroid cancer: a systematic review

INTRODUCTION

Thyroid cancer (TC) is an important common endocrine malignancy, and its incidence has increased in the past decades. The current TC diagnosis and classification tools are fine-needle aspiration (FNA) and histological examination following thyroidectomy. The metabolite profile alterations of thyroid cells (oncometabolites) can be considered for current TC diagnosis and management protocols.

METHODS

This systematic review focuses on metabolite alterations within the plasma, FNA specimens, and tissue of malignant TC contrary to benign, goiter, or healthy TC samples. A systematic search of MEDLINE (PubMed), Scopus, Embase, and Web of Science databases was conducted, and the final 31 studies investigating metabolite biomarkers of TC were included.

RESULTS

A total of 15 targeted studies and 16 untargeted studies revealed several potential metabolite signatures of TC such as glucose, fructose, galactose, mannose, 2-keto-d-gluconic acid and rhamnose, malonic acid and inosine, cholesterol and arachidonic acid, glycosylation (immunoglobulin G [IgG] Fc-glycosylation), outer mitochondrial membrane 20 (TOMM20), monocarboxylate transporter 4 (MCT4), choline, choline derivatives, myo-/scyllo-inositol, lactate, fatty acids, several amino acids, cell membrane phospholipids, estrogen metabolites such as 16 alpha-OH E1/2-OH E1 and catechol estrogens (2-OH E1), and purine and pyrimidine metabolites, which were suggested as the TC oncometabolite.

CONCLUSION

Citrate was suggested as the first most significant biomarker and lactate as the second one. Further research is needed to confirm these biomarkers as the TC diagnostic oncometabolite.

Investigation of the urinary metabolic variations and the application in bladder cancer biomarker discovery

Urine metabolomics have been used to identify biomarkers for clinical diseases. However, inter-individual variations and effect factors need to be further evaluated. In our study, we explored the urine metabolome in a cohort of 203 health adults, 6 patients with benign bladder lesions, and 53 patients with bladder cancer (BCa) using liquid chromatography coupled with high resolution mass spectrometry. Inter-individual analysis of both healthy controls and BCa patients showed that the urine metabolome was relatively stable. Further analysis indicated that sex and age affect inter-individual variations in urine metabolome. Metabolic pathways such as tryptophan metabolism, the citrate cycle, and pantothenate and CoA biosynthesis were found to be related to sex and age. To eliminate age and sex interference, additional BCa urine metabolomic biomarkers were explored using age and sex-matched urine samples (Test group: 44 health adults vs. 33 patients with BCa). Metabolic profiling of urine could significantly differentiate the cases with cancer from the controls and high-grade from low-grade BCa. A metabolite panel consisting of trans-2-dodecenoylcarnitine, serinyl-valine, feruloyl-2-hydroxyputrescine, and 3-hydroxynonanoyl carnitine were discovered to have good predictive ability for BCa with an area under the curve (AUC) of 0.956 (cross validation: AUC = 0.924). A panel of indolylacryloylglycine, N₂ -galacturonyl-L-lysine, and aspartyl-glutamate was used to establish a robust model for high- and low-grade BCa distinction with AUC of 0.937 (cross validation: AUC = 0.891). External sample (26 control vs. 20 BCa) validation verified the acceptable accuracy of these models for BCa detection. Our study showed that urinary metabolomics is a useful strategy for differential analysis and biomarker discovery.

Metabolomics profiling in plasma samples from glioma patients correlates with tumor phenotypes

Background

Tumor-based molecular biomarkers have redefined in the classification gliomas. However, the association of systemic metabolomics with glioma phenotype has not been explored yet.

Methods

In this study, we conducted two-step (discovery and validation) metabolomic profiling in plasma samples from 87 glioma patients. The metabolomics data were tested for correlation with glioma grade (high vs low), glioblastoma (GBM) versus malignant gliomas, and IDH mutation status.

Results

Five metabolites, namely uracil, arginine, lactate, cystamine, and ornithine, significantly differed between high- and low-grade glioma patients in both the discovery and validation cohorts. When the discovery and validation cohorts were combined, we identified 29 significant metabolites with 18 remaining significant after adjusting for multiple comparisons. Those 18 significant metabolites separated high- from low-grade glioma patients with 91.1% accuracy. In the pathway analysis, a total of 18 significantly metabolic pathways were identified. Similarly, we identified 2 and 6 metabolites that significantly differed between GBM and non-GBM, and IDH mutation positive and negative patients after multiple comparison adjusting. Those 6 significant metabolites separated IDH1 mutation positive from negative glioma patients with 94.4% accuracy. Three pathways were identified to be associated with IDH mutation status. Within arginine and proline metabolism, levels of intermediate metabolites in creatine pathway were all significantly lower in IDH mutation positive than in negative patients, suggesting an increased activity of creatine pathway in IDH mutation positive tumors.

Conclusion

Our findings identified metabolites and metabolic pathways that differentiated tumor phenotypes. These may be useful as host biomarker candidates to further help glioma molecular classification.

Metabolomic analysis of percutaneous fine-needle aspiration specimens of thyroid nodules: Potential application for the preoperative diagnosis of thyroid cancer

Thyroid nodules are a very common problem. Since malignant thyroid nodules should be treated surgically, preoperative diagnosis of thyroid cancer is very crucial. Cytopathologic analysis of percutaneous fine-needle aspiration (FNA) specimens is the current gold standard for diagnosing thyroid nodules. However, this method has led to high rates of inconclusive results. Metabolomics has emerged as a useful tool in medical fields and shown great potential in diagnosing various cancers. Here, we evaluated the potential of nuclear magnetic resonance (NMR) analysis of percutaneous FNA specimens for preoperative diagnosis of thyroid cancer. We analyzed metabolome of FNA samples of papillary thyroid carcinoma (n = 35) and benign follicular nodule (n = 69) using a proton NMR spectrometer. The metabolomic profiles showed a considerable discrimination between benign and malignant nodules. Receiver operating characteristic (ROC) curve analysis indicated that seven metabolites could serve as discriminators (area under ROC curve value, 0.64–0.85). These findings demonstrated that NMR analysis of percutaneous FNA specimens of thyroid nodules can be potentially useful in the accurate and rapid preoperative diagnosis of thyroid cancer.

Plasma metabolomic profile and potential biomarkers for missed abortion

A missed abortion (MA) is an in utero death of the embryo or fetus before the 20th week of gestation with retained products of conception, and this condition is currently common in China. In order to discover novel biomarkers for MA, ultrahigh performance liquid chromatography was applied to study plasma metabolite profiles for 33 patients with MA and 29 control subjects. Thirty-seven differential plasma metabolites were found to discriminate between the two groups in the initial cohort (15 subjects with MA and 15 healthy controls). The feasibility of using these potential biomarkers to predict MA was further evaluated in the validation cohort (18 subjects with MA and 14 healthy controls) and 15 had an area under the receiver operating characteristic curve of >0.80, making them satisfactory. Tryptophan metabolism and sphingolipid metabolism were identified as important potential target pathways for MA using metabolic pathway impact analysis. Furthermore, three of the 15 satisfactory metabolites (glyceric acid, indole and sphingosine) were combined to establish a predictive model with 100% sensitivity and 100% specificity in the validation cohort. Taken together, these results suggest that MA results in significant disturbance of metabolism and those various novel biomarkers have satisfactory diagnostic and predictive power for MA.

Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

INTRODUCTION

Metabolomics is the emerging member of "omics" sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

OBJECTIVES

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

METHODS

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

RESULTS

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

CONCLUSION

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.

Glucose metabolism in gastric cancer: The cutting-edge

Glucose metabolism in gastric cancer cells differs from that of normal epithelial cells. Upregulated aerobic glycolysis (Warburg effect) in gastric cancer meeting the demands of cell proliferation is associated with genetic mutations, epigenetic modification and proteomic alteration. Understanding the mechanisms of aerobic glycolysis may contribute to our knowledge of gastric carcinogenesis. Metabolomic studies offer novel, convenient and practical tools in the search for new biomarkers for early detection, diagnosis, prognosis, and chemosensitivity prediction of gastric cancer. Interfering with the process of glycolysis in cancer cells may provide a new and promising therapeutic strategy for gastric cancer. In this article, we present a brief review of recent studies of glucose metabolism in gastric cancer, with primary focus on the clinical applications of new biomarkers and their potential therapeutic role in gastric cancer.

Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer

BACKGROUND

Untargeted metabolomics was used in case-control studies of adenocarcinoma (ADC) lung cancer to develop and test metabolite classifiers in serum and plasma as potential biomarkers for diagnosing lung cancer.

METHODS

Serum and plasma were collected and used in two independent case-control studies (ADC1 and ADC2). Controls were frequency matched for gender, age, and smoking history. There were 52 adenocarcinoma cases and 31 controls in ADC1 and 43 adenocarcinoma cases and 43 controls in ADC2. Metabolomics was conducted using gas chromatography time-of-flight mass spectrometry. Differential analysis was performed on ADC1 and the top candidates (FDR < 0.05) for serum and plasma used to develop individual and multiplex classifiers that were then tested on an independent set of serum and plasma samples (ADC2).

RESULTS

Aspartate provided the best accuracy (81.4%) for an individual metabolite classifier in serum, whereas pyrophosphate had the best accuracy (77.9%) in plasma when independently tested. Multiplex classifiers of either 2 or 4 serum metabolites had an accuracy of 72.7% when independently tested. For plasma, a multimetabolite classifier consisting of 8 metabolites gave an accuracy of 77.3% when independently tested. Comparison of overall diagnostic performance between the two blood matrices yielded similar performances. However, serum is most ideal given higher sensitivity for low-abundant metabolites.

CONCLUSION

This study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted.

IMPACT

These biomarkers could improve early detection and diagnosis of lung cancer.